scholarly journals Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable

2012 ◽  
Vol 31 (2) ◽  
pp. 153-163 ◽  
Author(s):  
T. Rohlfing
2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  

2004 ◽  
Vol 43 (04) ◽  
pp. 367-370 ◽  
Author(s):  
U. Morgenstern ◽  
R. Steinmeier ◽  
F. Uhlemann

Summary Objective: The registration of medical volume data sets plays an important role when different images or modalities are used during computer-assisted surgical procedures. Nevertheless, it is often questionable how robust and accurate the underlying algorithms really are. Therefore, the goal is to foster the establishment of methods for an objective evaluation. Method: To reliably calculate the accuracy of registration algorithms, a reference transformation must be known. Due to the unknown perfect registration for real clinical data, the simulation of realistic data and successive affine transformations are employed. The simulation is based on models of the respective imaging modality where the dominant physical effects are taken into account. This gives the user full control over all simulation and transformation parameters. Finally, suitable quality measures are applied which allow a systematic evaluation of image registration accuracy by comparing the known theoretical result and the transformation calculated by the algorithm under investigation. Results: During the development of a new registration algorithm, the presented method proved to be a very valuable tool for optimization and evaluation of registration accuracy, since it allows objective numerical comparison of the calculated results. Conclusions: The presented method can be used during the development of algorithms for optimization and for quantitative comparison of different registration schemes. The respective software tool can automatically generate and transform simulated but realistic data. Employing suitable numerical quality measures, an objective evaluation of registration results can be easily obtained. Still, the validity of the relatively simple models has to be verified to draw reliable conclusions with respect to real data.


2020 ◽  
Vol 47 (7) ◽  
pp. 3023-3031
Author(s):  
Hisamichi Takagi ◽  
Noriyuki Kadoya ◽  
Tomohiro Kajikawa ◽  
Shohei Tanaka ◽  
Yoshiki Takayama ◽  
...  

2017 ◽  
Vol 42 ◽  
pp. 108-111 ◽  
Author(s):  
Hideharu Miura ◽  
Shuichi Ozawa ◽  
Minoru Nakao ◽  
Kengo Furukawa ◽  
Yoshiko Doi ◽  
...  

Author(s):  
Daisuke Deguchi ◽  
Kensaku Mori ◽  
Yasuhito Suenaga ◽  
Jun-ichi Hasegawa ◽  
Jun-ichiro Toriwaki ◽  
...  

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